Time Series Forecasting

Time series forecasting aims to predict future values based on historical data, crucial for diverse applications from finance to healthcare. Current research emphasizes improving model accuracy and efficiency, focusing on transformer-based architectures, state-space models like Mamba, and hybrid approaches combining their strengths, as well as exploring data augmentation and explainable AI techniques. These advancements are driving improvements in forecasting accuracy and interpretability, leading to better decision-making across various sectors and contributing to a deeper understanding of complex temporal dynamics.

Papers

November 28, 2022

Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting
Arindam Jati, Vijay Ekambaram, Shaonli Pal, Brian Quanz, Wesley M. Gifford, Pavithra Harsha, Stuart Siegel, Sumanta Mukherjee, Chandra Narayanaswami
Time Series Forecasting Hyperparameter Optimization Related Hyperparameters Hierarchical Forecasting Forecasting Competition

November 22, 2022

Time Series Forecasting with Hypernetworks Generating Parameters in Advance
Jaehoon Lee, Chan Kim, Gyumin Lee, Haksoo Lim, Jeongwhan Choi, Kookjin Lee, Dongeun Lee, Sanghyun Hong, Noseong Park
Time Series Forecasting Temporal Pattern Data Drift Non Graph Hypernetwork Baseline Conditioned Hypernetwork Future Data

November 5, 2022

A Comprehensive Survey of Regression Based Loss Functions for Time Series Forecasting
Aryan Jadon, Avinash Patil, Shruti Jadon
Loss Function Time Series Forecasting Comprehensive Survey Long Term Forecasting

October 31, 2022

Probabilistic Decomposition Transformer for Time Series Forecasting
Junlong Tong, Liping Xie, Wankou Yang, Kanjian Zhang
Transformer Based Model Time Series Forecasting Probabilistic Forecast Interpretable Forecast Probabilistic Transformer

October 27, 2022

Forecasting Graph Signals with Recursive MIMO Graph Filters
Jelmer van der Hoeven, Alberto Natali, Geert Leus
Graph Drawing Time Series Forecasting Graph Signal Processing Graph Filter Graph Product Graph Forecasting

October 26, 2022

TILDE-Q: A Transformation Invariant Loss Function for Time-Series Forecasting
Hyunwook Lee, Chunggi Lee, Hongkyu Lim, Sungahn Ko
Time Series Forecasting Time Series Data Temporal Dynamic Invariant Loss Shape Aware Loss Function

October 25, 2022

WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting
Youngin Cho, Daejin Kim, Dongmin Kim, Mohammad Azam Khan, Jaegul Choo
Time Series Forecasting Deep Network Regularization Technique Error Bound Recent Deep Learning

October 22, 2022

SpectraNet: Multivariate Forecasting and Imputation under Distribution Shifts and Missing Data
Cristian Challu, Peihong Jiang, Ying Nian Wu, Laurent Callot
Time Series Forecasting Distribution Shift Missing Data Knowledge Enhanced Conditional Imputation Forecasting Task Multivariate Forecasting Deep Spectral

October 20, 2022

Multimodal Neural Network For Demand Forecasting
Nitesh Kumar, Kumar Dheenadayalan, Suprabath Reddy, Sumant Kulkarni
Time Series Forecasting Demand Forecasting Open Checkout Free Grocery Multimodal Neural Network Forecasting Application

October 17, 2022

Flipped Classroom: Effective Teaching for Time Series Forecasting
Philipp Teutsch, Patrick Mäder
Time Series Forecasting LSTM Network Curriculum Learning Sequence to Sequence Model Training Iteration

October 12, 2022

Regularized Graph Structure Learning with Semantic Knowledge for Multi-variates Time-Series Forecasting
Hongyuan Yu, Ting Li, Weichen Yu, Jianguo Li, Yan Huang, Liang Wang, Alex Liu
Time Series Forecasting Graph Generation Semantic Knowledge Implicit Graph Graph Forecasting Graphical Model Prior

October 6, 2022

Temporal Spatial Decomposition and Fusion Network for Time Series Forecasting
Liwang Zhou, Jing Gao
Time Series Forecasting Fusion Network Learning Decomposition Temporal Decomposition Time Series Decomposition

October 4, 2022

MTSMAE: Masked Autoencoders for Multivariate Time-Series Forecasting
Peiwang Tang, Xianchao Zhang
Supervised Autoencoder Time Series Forecasting Multivariate Time Series Masked Autoencoders Self Supervised Transformer

October 2, 2022

Grouped self-attention mechanism for a memory-efficient Transformer
Bumjun Jung, Yusuke Mukuta, Tatsuya Harada
Time Series Forecasting Time Series Analysis Group Setting Memory Efficient Long Range Dependency

September 28, 2022

September 27, 2022

Retrieval Based Time Series Forecasting
Baoyu Jing, Si Zhang, Yada Zhu, Bin Peng, Kaiyu Guan, Andrew Margenot, Hanghang Tong
Time Series Forecasting Retrieval Augmented Time Series Imputation Time sEries Deep Learning Forecasting

September 21, 2022

Review of Time Series Forecasting Methods and Their Applications to Particle Accelerators
Sichen Li, Andreas Adelmann
Financial Application Narrative Review Data Driven Time Series Forecasting Multivariate Time Series Particle Accelerator Time Series Forecasting Method

September 20, 2022